
Discovery I. There indeed exist common artifacts across different diffusion models.
Furthermore, using text information in the proposed hybrid detection better benefits the detector to locate these common artifacts and achieves better performance, especially encountering open-world fake images.

Discovery II. Different diffusion models indeed generate distinctive fingerprints together with their images, which allow us to attribute fake images to their sources.
We further visualize these unique fingerprints using frequency spectra inspired by Zhang et al. [1]}.

Discovery III. For regular sentences, their structures does not affect the authenticity of the generated image, whereas extreme structures tend to generate low-authenticity images.
Orthogonally, semantics matter the image authenticity.
For example, descriptions about “person” often produce higher-authenticity images.

